In recent years, studies have shown that reconfigurable antennas can offer additional performance gains in Multiple Input Multiple Output (MIMO) systems by increasing the channel capacity, diversity order and even have been shown to perform well in the low SNR regimes. These reconfigurable antennas are capable of generating multiple uncorrelated channel realizations by changing their electrical and radiation properties and are gradually making their way into commercial wireless systems. The key to effectively utilizing the reconfigurability offered by these antennas is to select a state that provides improvement in received SNR, throughput or channel capacity (referred to as “optimal state” herein) among all the states for a given wireless environment.
Reconfigurable antennas can be employed either at the transmitter or the receiver, or at both ends of the RF chain. This flexibility can create a large search space in order to find an optimal state for communication. The key bottleneck to exploit the full potential of reconfigurable antennas is the requirement of additional training to obtain the channel state information corresponding to each beam pattern and/or the combination thereof at the receiver and transmitter. Moreover, the effect of node mobility to a different location, changes in physical antenna orientation, and the dynamic nature of the wireless channel can render previously found “optimal” states suboptimal over time. This makes it important for a wireless system to employ a learning algorithm to find the new optimal states and to maintain the highest possible SNR.
In order to be effective, an online learning algorithm for antenna state selection (also referred to herein interchangeably as “selection technique”) must overcome certain challenges, including:                1) Optimal antenna state for each wireless link (between a single transmitter and a receiver location) is unknown a priori. Moreover, each wireless link may have a different optimal state. A selection technique should be able to learn and find the optimal state for a given link.        2) For a given wireless link, there might be several states which are near optimal over time, based on channel conditions and multi-path propagation. A selection technique should provide a policy to balance between exploiting a known successful state and exploring other available states to account for dynamic behavior of the channel.        3) For the purpose of real-time implementation in a practical wireless system, a selection technique must employ simple metrics that can be extracted from the channel without large overhead or requiring extensive feedback data.        4) The selection technique should require reduced training or reduced channel state information to keep the overhead low in a practical wireless system.        
Previous work related to state selection is based on estimating channel response of each antenna state which required changing the standard OFDM frame format. However, as the number of states increases, the scheme becomes impractical. See, e.g., A. Grau, H. Jafarkhani, and F. De Flavis, “A reconfigurable multiple-input multiple-output communication system,” IEEE Transactions on Wireless Communications, vol. 7, no. 5, pp. 1719-1733, 2008. Selection techniques using second order channel statistics and average SNR information have also been proposed by D. Piazza, M. D'Amico, and K. Dandekar in “Performance improvement of a wideband MIMO system by using two-port RLWA,” Antennas and Wireless Propagation Letters, IEEE, vol. 8, pp. 830-834, 2009. Further H. Eslami, C. Sukumar, D. Rodrigo, S. Mopidevi, A. Eltawil, L. Jofre, and B. Cetiner, proposed training schemes with reduced overhead and compared these to exhaustive search techniques in “Reduced overhead training for multi reconfigurable antennas with beam-tilting capability,” IEEE Transactions on Wireless Communications, vol. 9, pp. 3810-3821, 2010. Though some of these techniques were successful in showing the benefits of multi-state selection and motivated the need for a selection algorithm, none solved the challenges mentioned above and were not truly adaptive in operation and required additional parameter tuning to perform optimally. Previous work in learning for cognitive radios has primarily been focused on link adaptation. See, e.g., R. Daniels, C. Caramanis, and R. Heath, “Adaptation in convolutionally coded MIMO-OFDM wireless systems through supervised learning and SNR ordering,” IEEE Transaction on Vehicular Technology, vol. 59, no. 1, pp. 114-126, 2010, and S. Yun and C. Caramanis, “Reinforcement learning for link adaptation in MIMO-OFDM wireless systems,” in GLOBECOM 2010, 2010 IEEE Global Telecommunications Conference, Dec. 2010, pp. 1-5 and channel allocation for dynamic spectrum access in Y. Gai, B. Krishnamachari, and R. Jain, “Learning multi-user channel allocations in cognitive radio networks: a combinatorial multi-armed bandit formulation,” in 2010 IEEE Symposium on New Frontiers in Dynamic Spectrum, IEEE, 2010, pp. 1-9.
It is desired to develop learning algorithms for antenna state selection to address the above challenges to improve the performance of wireless systems and to investigate the feasibility of implementing such algorithms in a practical wireless system. The present invention addresses these needs in the art.